@inproceedings{61492, abstract = {{This paper deals with the development and results of a prediction framework for traffic light control systems as well as the usage and benefits of such predictions in green light optimal speed advisory (GLOSA) scenarios. Various machine learning methods like support vector machines, neural networks or reinforcement learning were evaluated for their applicability in the prediction context and compared based on their efficiency and most importantly accuracy. The resulting prediction framework uses decision tree ensemble models combined with certain model knowledge to forecast different control strategies. This method was chosen due to its best performance in various test scenarios. Very high accuracy and fidelity were achieved for standard control methods like fixed-time, time-of-day-based and 'ordinary' traffic-based programs. Only for the more sophisticated model predictive control which was tested lower accuracies were achieved. For the upcoming GLOSA application the penetration of equipped vehicles was varied for different traffic scenarios and control strategies. Results showcase high potentials for enhancing urban mobility and reducing environmental impact by lower emissions and waiting times. However, it is also clear from the studies presented in this contribution that the coordination of the control strategy with the GLOSA vehicles is of enormous importance.}}, author = {{Malena, Kevin and Link, Christopher and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)}}, keywords = {{ML, Prediction, Tree Ensembles, GLOSA}}, location = {{Gold Coast (Australia)}}, publisher = {{IEEE}}, title = {{{ML-based Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application}}}, volume = {{28}}, year = {{2026}}, } @inproceedings{59088, abstract = {{This paper deals with the implementation and results of the application of a multi-stage traffic light control system which includes a simulation-based traffic estimation and model predictive control. The traffic light control system incorporates a fuzzy system for traffic light phase preselection, followed by a model predictive control to optimise phase combinations and switching times. Predefined phases are selected without restrictions in the order according to a multi-objective optimisation to adapt to the traffic as freely as possible. Initially, the system is tested in simulations and compared with existing methods and analysed afterwards for its effectiveness in a prototype commissioning in field tests. Results indicate high potentials for reducing emissions and waiting times, highlighting the system's value. However, further refinement is necessary for standard implementation. This comprehensive approach demonstrates advancements in traffic management technology, showcasing the potential for enhancing urban mobility and reducing environmental impact.}}, author = {{Malena, Kevin and Link, Christopher and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)}}, issn = {{2153-0017}}, keywords = {{MPC}}, location = {{Edmonton (Canada)}}, publisher = {{IEEE}}, title = {{{Implementation and Results of a Multi-Stage Model Predictive Traffic Light Control System}}}, doi = {{10.1109/itsc58415.2024.10919569}}, volume = {{27}}, year = {{2025}}, } @inproceedings{44390, abstract = {{The development of autonomous vehicles and their introduction in urban traffic offer many opportunities for traffic improvements. In this paper, an approach for a future traffic control system for mixed autonomy traffic environments is presented. Furthermore, a simulation framework based on the city of Paderborn is introduced to enable the development and examination of such a system. This encompasses multiple elements including the road network itself, traffic lights, sensors as well as methods to analyse the topology of the network. Furthermore, a procedure for traffic demand generation and routing is presented based on statistical data of the city and traffic data obtained by measurements. The resulting model can receive and apply the generated control inputs and in turn generates simulated sensor data for the control system based on the current system state.}}, author = {{Link, Christopher and Malena, Kevin and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems}}, isbn = {{978-989-758-652-1}}, keywords = {{Traffic Simulation, Traffic Control, Car2X, Mixed Autonomy, Autonomous Vehicles, SUMO, Sensor Simulation, Traffic Demand Generation, Routing, Traffic Lights, Graph Analysis, Traffic Observer}}, location = {{Prague, Czech Republic}}, publisher = {{SCITEPRESS - Science and Technology Publications}}, title = {{{Simulation Environment for Traffic Control Systems Targeting Mixed Autonomy Traffic Scenarios}}}, doi = {{10.5220/0011987600003479}}, year = {{2023}}, } @misc{56827, abstract = {{Zusammenfassung: Die Erfindung betrifft ein Verkehrsleitsystem für die Steuerung von Lichtsignalanlagen aufweisend: • Eingänge für Verkehrszustandsermittlungsverfahren abhängig von verschiedenen Sensoren, wobei die Sensoren einen aktuellen Verkehrszustand eines zugeordneten Verkehrsabschnitts ermitteln, • Statusregister, die den aktuellen Status der gesteuerten Lichtsignalanlagen speichern, • Phasenregister, die alle möglichen Verkehrsleitphasen, die durch die Lichtsignalanlagen angesteuert werden können, speichern, • eine Fuzzy-Logik (F) zur Vorselektion von möglichen Verkehrsleitphasen basierend auf einem aktuellen Verkehrszustand, wobei für jede mögliche Verkehrsleitphase basierend auf dem aktuellen Verkehrszustand ein Prioritätswert ermittelt wird, wobei für die weitere Verarbeitung nur eine vorbestimmte Anzahl von vorselektierten nachfolgenden Verkehrsleitphasen anhand der Priorität ausgewählt wird, • wobei die so vorausgewählten Verkehrsleitphasen einer modellprädiktiven Regelung mit einer Verkehrsprädiktionssimulation (MPC) zugeführt werden, wobei die Regelung basierend auf den Phasenfolgen (gebildet aus den vorselektierten Verkehrsleitphasen), dem aktuellen Verkehrszustand und dem aktuellen Status der gesteuerten Lichtsignalanlagen eine geeignete prädiktive zeitliche Phasensteuerung ermittelt, wobei die Schaltzeitpunkte der Phasenfolgen durch ein Optimierungsverfahren berechnet werden, • wobei die so ermittelte prädiktive zeitliche Phasensteuerung keine, eine oder mehrere zu schaltenden Folgephasen mit den ermittelten Schaltzeitpunkten aufweist, • wobei anhand der Bewertungen der einzelnen prädiktiven zeitlichen Phasensteuerungen ermittelt wird, welche der prädiktiven zeitlichen Phasensteuerungen zur weiteren Steuerung der Lichtsignalanlagen ausgewählt und verwendet wird.}}, author = {{Malena, Kevin and Link, Christopher and Gausemeier, Sandra and Trächtler, Ansgar}}, title = {{{Vorrichtung und Verfahren zur echtzeit-basierten dynamischen Verkehrszuordnung für zumindest zwei nachfolgende Fahrbahnen}}}, year = {{2023}}, } @inbook{33849, abstract = {{Modern traffic control systems are key to cope with current and future traffic challenges. In this paper information obtained from a microscopic traffic estimation using various data sources is used to feed a new developed traffic control approach. The presented method can control a traffic area with multiple traffic light systems (TLS) reacting to individual road users and pedestrians. In contrast to widespread green time extension techniques, this control selects the best phase sequence by analyzing the current traffic state reconstructed in SUMO and its predicted progress. To achieve this, the key aspect of the control strategy is to use Model Predictive Control (MPC). In order to maintain realism for real world applications, among other things, the traffic phase transitions are modelled in detail and integrated within the prediction. For the efficiency, the approach incorporates a fuzzy logic preselection of all phases reducing the computational effort. The evaluation itself is able to be easily adjusted to focus on various objectives like low occupancies, reducing waiting times and emissions, few number of phase transitions etc. determining the best switching times for the selected phases. Exemplary traffic simulations demonstrate the functionality of the MPC-based control and, in addition, some aspects under development like the real-world communication network are also discussed.}}, author = {{Malena, Kevin and Link, Christopher and Bußemas, Leon and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{Communications in Computer and Information Science}}, editor = {{Klein, Cornel and Jarke, Mathias and Helfert, Markus and Berns, Karsten and Gusikhin, Oleg}}, isbn = {{9783031170973}}, issn = {{1865-0929}}, keywords = {{Traffic control, Traffic estimation, Real-time, MPC, Fuzzy, Isolated intersection, Networked intersection, Sensor fusion}}, pages = {{232–254}}, publisher = {{Springer International Publishing}}, title = {{{Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments}}}, doi = {{10.1007/978-3-031-17098-0_12}}, volume = {{1612}}, year = {{2022}}, } @inproceedings{24159, abstract = {{The online fitting of a microscopic traffic simulation model to reconstruct the current state of a real traffic area can be challenging depending on the provided data. This paper presents a novel method based on limited data from sensors positioned at specific locations and guarantees a general accordance of reality and simulation in terms of multimodal road traffic counts and vehicle speeds. In these considerations, the actual purpose of research is of particular importance. Here, the research aims at improving the traffic flow by controlling the Traffic Light Systems (TLS) of the examined area which is why the current traffic state and the route choices of individual road users are the matter of interest. An integer optimization problem is derived to fit the current simulation to the latest field measurements. The concept can be transferred to any road traffic network and results in an observation of the current multimodal traffic state matching at the given sensor position. First case studies show promosing results in terms of deviations between reality and simulation.}}, author = {{Malena, Kevin and Link, Christopher and Mertin, Sven and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{VEHITS 2021 Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems}}, isbn = {{978-989-758-513-5}}, keywords = {{Microscopic Traffic Simulation, Online State Estimation, Mixed Road Users, Sensor Fusion, Integer Programming, Route Choice, Vehicle2Infrastructure}}, location = {{Online Streaming}}, pages = {{386--395}}, publisher = {{SCITEPRESS}}, title = {{{Online State Estimation for Microscopic Traffic Simulations using Multiple Data Sources*}}}, volume = {{7}}, year = {{2021}}, } @inproceedings{24166, abstract = {{This paper deals with a novel method for the online fitting of a microscopic traffic simulation model to the current state of a real world traffic area. The traffic state estimation is based on limited data of different measurement sources and guarantees general accordance of reality and simulation in terms of multimodal road traffic counts and vehicle speeds. The research is embedded in the challenge of improving the traffic by controlling the traffic light systems (TLS) of the examined area. Therefore, the current traffic state and the predicted route choices of individual road users are the matter of interest. The concept is generally transferable to any road traffic system. To give an impression of the accuracy and potential of the approach, the validation and first application results are presented.}}, author = {{Malena, Kevin and Link, Christopher and Mertin, Sven and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{2021 IEEE Transportation Electrification Conference & Expo (ITEC)}}, isbn = {{978-1-7281-7584-3}}, publisher = {{IEEE}}, title = {{{Validation of an Online State Estimation Concept for Microscopic Traffic Simulations◆}}}, doi = {{10.1109/itec51675.2021.9490087}}, year = {{2021}}, } @inproceedings{22966, author = {{Mertin, Sven and Malena, Kevin and Link, Christopher and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{The 23rd IEEE International Conference on Intelligent Transportation Systems}}, publisher = {{International Conference on Intelligent Transportation Systems (ITSC)}}, title = {{{Macroscopic Traffic Flow Control using Consensus Algorithms}}}, volume = {{23}}, year = {{2020}}, } @inproceedings{22968, author = {{Biemelt, Patrick and Link, Christopher and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{Proceedings of the 21st IFAC World Congress}}, location = {{Berlin, Germany}}, pages = {{6082 -- 6088}}, title = {{{A Model-Based Online Reference Prediction Strategy for Model Predictive Motion Cueing Algorithms}}}, year = {{2020}}, }